A simulation method is one that attempts to model a real-world situation in order to learn something about it. Each object and action in the real situation has its counterpart in the computer method. If the simulation is accurate, that is, if the computer method successfully mirrors the real world, then the result of the computer method should mirror the result of the actions being simulated. Thus it is possible to understand what occurs in the real-world situation without actually observing its occurrence.
Based on the power of the computer and on the ability to build adequate models of reality, the simulation of experiments has become an increasingly effective and often a superior substitute for physical experimentation. For example, the building and testing of engineering prototypes is an experimentation effort that is done more and more in terms of models and simulations rather than by conventional means, i.e. the actual building on scaled-down models.
Concurrent Simulation (CS) is the simultaneous, side-by-side simulation of related experiments using one computer run. CS is a method that runs on conventional computers, performing concurrent experiments without concurrent hardware. That is to say, that CS uses a computer containing at least one central processing unit (CPU) rather than multiple CPU's. It applies and is limited to systems simulated with discrete events. Typically 10 to 1,000 times faster than serial (one-at-a-time) simulation of single experiments, its speed is largely based on the number of and similarities between experiments. CS dates from about 1970 and was first developed for fault simulation of gate-level digital networks. Over the years it has increased in generality and, more recently, evolved into a simulation methodology. Whenever discrete event simulation is the method chosen to solve a particular problem, CS is usually better than serial simulation. CS has several advantages over serial simulation.
First, all experiments advance synchronously through the dimension of time, and CS is therefore analogous to a race in which the experiments are competitors. This constitutes a race methodology and a comparative style of simulation. This methodology and the speed of CS permit the solution of problems more difficult and larger than with serial simulation. A simulation strategy based on this methodology and comparative style is to simulate and observe related experiments which are initially the same but later become different.
Second, observation, which is awkward and costly for serial simulation, is handled easily and elegantly with CS. The experiments are observed comparatively, and can be compared in exact detail as well statistically. Statistical "signatures" are maintained and periodically analyzed for all experiments.
Next, CS offers speed in various forms. Relative to serial simulation, experiments are compressed into a single run. The idle time between serial simulations is avoided and a simulation project is strategically accelerated. Also, due to the number of concurrent experiments, due to their similarity, and the similarity between them and a reference experiment, the CPU time, as mentioned previously, is typically 10 to 1,000 times less than the equivalent serial simulations. Further, based on the analysis of signatures, the initial reference experiment may often be replaced with a more central one which reduces the average differences between reference and concurrent experiments and gains additional speed.
Lastly, CS provides accuracy and generality. In fields such as biology and chemistry, for example, it is desirable to perform related and similar physical experiments in parallel, but it is normally too costly due to labor, space, equipment, and the raw materials that are needed. CS is a parallel (and precisely time-synchronous) form of experimentation, and is therefore an alternative to parallel physical experimentation. It requires no resources except a conventional computer and modeling/simulation skills.